{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5804f2c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import random\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bbb78a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 固定随机种子，保证结果可复现\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "\n",
    "# 检查 CUDA 是否可用\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch.utils.data import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cccc6933",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TimeSeriesDataset(Dataset):\n",
    "    def __init__(self, file_path, seq_len=20, forecast_horizon=1):\n",
    "        super(TimeSeriesDataset, self).__init__()\n",
    "        self.seq_len = seq_len\n",
    "        self.forecast_horizon = forecast_horizon\n",
    "\n",
    "        # 从文件读取数据\n",
    "        data = pd.read_csv(file_path).values\n",
    "        print(len(data))\n",
    "        data = data[::50]\n",
    "        print(len(data))\n",
    "        print(pd.DataFrame(data).head(10))\n",
    "        # 划分输入特征和预测目标\n",
    "        input_features = data[:, :3]\n",
    "        target_features = data[:, 3:5]\n",
    "\n",
    "        # 分别对输入特征和预测目标进行标准化归一化\n",
    "        self.input_mean = input_features.mean(axis=0)\n",
    "        self.input_std = input_features.std(axis=0)\n",
    "        self.input_data = (input_features - self.input_mean) / self.input_std\n",
    "\n",
    "        self.target_mean = target_features.mean(axis=0)\n",
    "        self.target_std = target_features.std(axis=0)\n",
    "        self.target_data = (target_features - self.target_mean) / self.target_std\n",
    "\n",
    "        # 构造样本：每个样本包含 seq_len 个历史时间步及对应的预测目标\n",
    "        self.samples = []\n",
    "        T = len(data) - seq_len - forecast_horizon + 1\n",
    "        for i in range(T):\n",
    "            x = self.input_data[i:i+seq_len, :]   # 历史序列\n",
    "            # 修正此处，一维数组切片\n",
    "            y = self.target_data[i+seq_len:i+seq_len+forecast_horizon]  # 预测目标\n",
    "            self.samples.append((x, y))\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.samples)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        x, y = self.samples[idx]\n",
    "        # 转换为 float32 型的 torch tensor，注意对于 CNN 输入需要 (channels, seq_len)\n",
    "        # 原始 x 形状为 (seq_len, feature)，转换后置为 (feature, seq_len)\n",
    "        return torch.tensor(x, dtype=torch.float32).transpose(0, 1), torch.tensor(y, dtype=torch.float32).squeeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "289d18c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14708349\n",
      "294167\n",
      "          0         1        2       3     4\n",
      "0  0.421797  0.041310 -0.90087  7030.0  0.95\n",
      "1  0.421797  0.041310 -0.90087  7030.0  0.95\n",
      "2  0.437163  0.057012 -0.90087  7030.0  0.95\n",
      "3  0.421797  0.057012 -0.90087  7030.0  0.95\n",
      "4  0.437163  0.041310 -0.90087  7030.0  0.95\n",
      "5  0.437163  0.057012 -0.90087  7030.0  0.95\n",
      "6  0.421797  0.057012 -0.90087  7030.0  0.95\n",
      "7  0.421797  0.041310 -0.90087  7030.0  0.95\n",
      "8  0.421797  0.057012 -0.90087  7030.0  0.95\n",
      "9  0.421797  0.041310 -0.90087  7030.0  0.95\n"
     ]
    }
   ],
   "source": [
    "# 设置参数\n",
    "SEQ_LEN = 20\n",
    "BATCH_SIZE = 32\n",
    "# 替换为你的数据文件路径\n",
    "file_path = '/root/bt/code/model_train_data/merged_data.csv'\n",
    "\n",
    "# 生成数据集\n",
    "dataset = TimeSeriesDataset(file_path, seq_len=SEQ_LEN, forecast_horizon=1)\n",
    "# 划分训练集和测试集\n",
    "train_size = int(0.8 * len(dataset))\n",
    "test_size = len(dataset) - train_size\n",
    "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])\n",
    "\n",
    "# 构建 DataLoader\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d77d85c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_loader 前 10 行数据：\n",
      "第 1 行: [tensor([[[ 1.0218,  1.0434,  1.0218,  ...,  1.0218,  1.0218,  1.0218],\n",
      "         [-0.7714, -0.7714, -0.7714,  ..., -0.7714, -0.7714, -0.7714],\n",
      "         [-0.2692, -0.2692, -0.2692,  ..., -0.2692, -0.2692, -0.2692]],\n",
      "\n",
      "        [[ 0.3127,  0.4006,  0.0930,  ...,  0.3347,  0.3567,  0.3347],\n",
      "         [ 2.4030,  2.2223,  2.8006,  ...,  2.1862,  2.4030,  2.4030],\n",
      "         [ 0.3461,  0.2615,  0.3179,  ...,  0.2051,  0.2333,  0.3179]],\n",
      "\n",
      "        [[ 1.0177,  0.1326,  0.6723,  ...,  0.7586,  0.6939,  0.7144],\n",
      "         [-0.5570,  0.0930,  0.1291,  ...,  1.4651,  1.8984,  0.9590],\n",
      "         [ 0.6679,  1.4237,  1.1110,  ...,  1.0849,  1.1110,  0.9021]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.5368, -1.5368, -1.5368,  ..., -1.5368, -1.5368, -1.5368],\n",
      "         [ 0.3897,  0.3897,  0.3897,  ...,  0.3897,  0.3897,  0.3897],\n",
      "         [-0.1617, -0.1617, -0.1617,  ..., -0.1357, -0.1617, -0.1617]],\n",
      "\n",
      "        [[-0.9891, -0.9891, -0.9891,  ..., -0.9891, -0.9891, -0.9891],\n",
      "         [-0.2747, -0.2385, -0.2747,  ..., -0.2385, -0.2385, -0.2385],\n",
      "         [-1.1898, -1.1898, -1.1898,  ..., -1.1898, -1.1898, -1.1898]],\n",
      "\n",
      "        [[ 1.0795,  1.0795,  1.0795,  ...,  1.0795,  1.0795,  1.0795],\n",
      "         [ 1.1007,  1.1007,  1.1007,  ...,  1.1007,  1.1007,  1.1007],\n",
      "         [ 0.6793,  0.6793,  0.6793,  ...,  0.6793,  0.7075,  0.6793]]]), tensor([[ 0.2219, -0.3199],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.2131, -0.3199],\n",
      "        [ 2.2516,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4235,  0.9008],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759]])]\n",
      "第 2 行: [tensor([[[ 1.0845,  1.0197,  1.0845,  ...,  0.9766,  0.9118,  0.9118],\n",
      "         [-1.1336, -0.8447, -1.0614,  ..., -0.8808, -0.9891, -1.0253],\n",
      "         [-0.2698,  0.0690, -0.2437,  ...,  0.4600,  0.4079,  0.4079]],\n",
      "\n",
      "        [[ 1.1252,  1.1250,  1.1250,  ...,  1.1030,  1.1252,  1.1252],\n",
      "         [-0.2716, -0.2356, -0.2356,  ..., -0.2356, -0.1993, -0.2355],\n",
      "         [-0.4444, -0.4449, -0.4449,  ..., -0.4731, -0.4444, -0.4444]],\n",
      "\n",
      "        [[ 1.1246,  1.1246,  1.1246,  ...,  1.1246,  1.1246,  1.1246],\n",
      "         [ 0.0894,  0.0894,  0.1255,  ...,  0.0894,  0.0894,  0.0894],\n",
      "         [ 0.8792,  0.8792,  0.8792,  ...,  0.8510,  0.8510,  0.8510]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.6723, -0.5212, -0.3485,  ..., -0.4996, -0.5428,  0.1696],\n",
      "         [ 1.1729,  1.2451,  0.5952,  ...,  0.0896,  0.0896, -1.1741],\n",
      "         [-1.3931, -1.3931, -1.5756,  ..., -1.4713, -1.6277, -2.5399]],\n",
      "\n",
      "        [[-1.1451, -1.1451, -1.1235,  ..., -1.1235, -1.1235, -1.1451],\n",
      "         [ 0.4274,  0.4274,  0.4274,  ...,  0.4274,  0.4274,  0.4274],\n",
      "         [-1.0991, -1.0991, -1.0991,  ..., -1.0991, -1.0991, -1.0991]],\n",
      "\n",
      "        [[-1.4895, -1.4895, -1.4895,  ..., -1.4679, -1.4895, -1.4895],\n",
      "         [-0.0892, -0.1253, -0.0892,  ..., -0.0892, -0.0531, -0.0892],\n",
      "         [-0.4539, -0.4539, -0.4539,  ..., -0.4799, -0.4799, -0.4539]]]), tensor([[-0.9536,  1.7146],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4238, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-2.4235,  0.9008],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.2131, -0.3199],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2131, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164]])]\n",
      "第 3 行: [tensor([[[-1.4206, -1.4206, -1.4206,  ..., -1.4206, -1.4206, -1.4206],\n",
      "         [ 0.5991,  0.5630,  0.5991,  ...,  0.5991,  0.5630,  0.5630],\n",
      "         [-0.6611, -0.6611, -0.6611,  ..., -0.6611, -0.6611, -0.6611]],\n",
      "\n",
      "        [[-0.9891, -0.9891, -0.9891,  ..., -0.9891, -0.9891, -0.9891],\n",
      "         [-0.2385, -0.2385, -0.2385,  ..., -0.2747, -0.2385, -0.2747],\n",
      "         [-1.1898, -1.1898, -1.1898,  ..., -1.1898, -1.1898, -1.1898]],\n",
      "\n",
      "        [[ 0.7705,  0.7265,  0.7048,  ...,  1.0343,  1.1881,  0.9684],\n",
      "         [ 0.6661,  0.6661,  0.5578,  ..., -0.1288, -0.5264, -0.4180],\n",
      "         [ 1.4652,  1.5498,  1.4939,  ...,  0.9300, -0.5080, -0.7054]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.5368, -1.5368, -1.5368,  ..., -1.5368, -1.5368, -1.5368],\n",
      "         [ 0.3897,  0.3897,  0.3897,  ...,  0.3897,  0.3897,  0.3897],\n",
      "         [-0.1357, -0.1357, -0.1357,  ..., -0.1357, -0.1357, -0.1357]],\n",
      "\n",
      "        [[ 0.7082,  0.7302,  0.7521,  ...,  0.7082,  0.7302,  0.7302],\n",
      "         [-0.2714, -0.2714, -0.3075,  ..., -0.3436, -0.4521, -0.4159],\n",
      "         [ 1.5021,  1.4457,  1.4457,  ...,  1.4457,  1.3893,  1.4457]],\n",
      "\n",
      "        [[-1.1235, -1.1235, -1.1235,  ..., -1.1235, -1.1235, -1.1235],\n",
      "         [ 1.5468,  1.5107,  1.5468,  ...,  1.5468,  1.5468,  1.5468],\n",
      "         [-0.7081, -0.7081, -0.7081,  ..., -0.7081, -0.7081, -0.6821]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 1.1243,  6.4957],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759]])]\n",
      "第 4 行: [tensor([[[-1.5597, -1.5597, -1.5377,  ..., -1.5597, -1.5377, -1.5377],\n",
      "         [-0.0575, -0.0575, -0.0575,  ..., -0.0575, -0.0575, -0.0575],\n",
      "         [ 0.1651,  0.1651,  0.1651,  ...,  0.1651,  0.1651,  0.1651]],\n",
      "\n",
      "        [[-1.1719, -1.1287, -1.1071,  ..., -1.0424, -1.0208, -1.0424],\n",
      "         [ 1.7851,  1.8573,  1.8573,  ...,  1.8934,  1.8934,  1.8934],\n",
      "         [-0.3776, -0.3254, -0.3515,  ..., -0.5340, -0.5340, -0.5340]],\n",
      "\n",
      "        [[ 0.8285,  1.4546,  1.2819,  ...,  0.9797,  1.1092,  1.0660],\n",
      "         [-0.8430,  1.3235, -0.3014,  ..., -0.5181, -0.0848,  0.0958],\n",
      "         [ 0.3567, -0.3992, -0.3992,  ...,  0.4609,  0.0960,  0.4088]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.5029, -1.5029, -1.5245,  ..., -1.5461, -1.4813, -1.5245],\n",
      "         [ 0.3007,  0.3368,  0.3007,  ...,  0.2285,  0.1924,  0.1924],\n",
      "         [ 0.4542,  0.4542,  0.4542,  ...,  0.5324,  0.5064,  0.5324]],\n",
      "\n",
      "        [[ 1.1215,  1.1215,  1.0996,  ...,  1.0996,  1.0996,  1.0776],\n",
      "         [ 0.0515,  0.0876,  0.0515,  ...,  0.0876,  0.0876,  0.0876],\n",
      "         [-0.5377, -0.5941, -0.5659,  ..., -0.5941, -0.5941, -0.6223]],\n",
      "\n",
      "        [[-1.5265, -1.5265, -1.5049,  ..., -1.5265, -1.5265, -1.5265],\n",
      "         [-0.3025, -0.3025, -0.3025,  ..., -0.2664, -0.2303, -0.2303],\n",
      "         [-0.0349, -0.0349, -0.0349,  ..., -0.0610, -0.0610, -0.0870]]]), tensor([[-0.3710, -0.6759],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-2.4235,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-2.1291,  2.4267],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9578, -0.1164]])]\n",
      "第 5 行: [tensor([[[-0.8655, -0.8655, -0.8645,  ..., -0.8655, -0.8655, -0.8655],\n",
      "         [-0.0064, -0.0064, -0.0059,  ..., -0.0064, -0.0064, -0.0064],\n",
      "         [-1.3340, -1.3340, -1.3337,  ..., -1.3340, -1.3340, -1.3600]],\n",
      "\n",
      "        [[-0.5928, -0.5928, -0.5928,  ..., -0.5928, -0.5928, -0.5928],\n",
      "         [ 0.7378,  0.7378,  0.7378,  ...,  0.7378,  0.7016,  0.7016],\n",
      "         [-1.4697, -1.4697, -1.4697,  ..., -1.4697, -1.4697, -1.4697]],\n",
      "\n",
      "        [[-1.5841, -1.4546, -1.1524,  ..., -1.5625, -1.9079, -1.4330],\n",
      "         [ 0.5924,  0.3396,  0.7368,  ...,  1.0979,  2.5061,  1.7117],\n",
      "         [ 0.1691,  0.1431, -0.0394,  ...,  0.1691,  0.6383,  0.3776]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.6867, -0.6856, -0.6856,  ..., -0.5140, -0.5140, -0.5356],\n",
      "         [-1.8563, -1.8557, -1.8557,  ..., -1.9285, -1.9646, -1.9285],\n",
      "         [ 0.2772,  0.2775,  0.2775,  ..., -0.1398, -0.1398, -0.1137]],\n",
      "\n",
      "        [[ 0.8285,  0.8501,  0.8285,  ...,  0.8501,  0.8501,  0.8501],\n",
      "         [-0.6986, -0.7347, -0.6986,  ..., -0.6986, -0.7347, -0.7347],\n",
      "         [ 0.9301,  0.9301,  0.9301,  ...,  0.9301,  0.9301,  0.9040]],\n",
      "\n",
      "        [[-0.8635, -0.6044,  0.5397,  ..., -0.8203, -0.8851, -0.9714],\n",
      "         [-1.3891, -1.1003, -0.3781,  ..., -1.4613, -1.5336, -1.4252],\n",
      "         [-0.7666, -0.3235, -1.2097,  ..., -0.7406, -0.5842, -0.7406]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.1291,  2.4267],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.3842,  0.3922],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 1.1243,  6.4957],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 1.3813, -0.1164]])]\n",
      "第 6 行: [tensor([[[ 0.4318,  0.4318,  0.4318,  ...,  0.4318,  0.4318,  0.4318],\n",
      "         [ 0.3919,  0.3558,  0.3919,  ...,  0.3919,  0.3919,  0.3558],\n",
      "         [-1.3855, -1.4115, -1.4115,  ..., -1.4115, -1.4115, -1.4115]],\n",
      "\n",
      "        [[ 1.0574,  1.0574,  1.0574,  ...,  1.0793,  1.0793,  1.0793],\n",
      "         [ 1.1006,  1.1006,  1.1006,  ...,  1.1006,  1.1006,  1.1006],\n",
      "         [ 0.6788,  0.6788,  0.6788,  ...,  0.6788,  0.6788,  0.7070]],\n",
      "\n",
      "        [[ 0.7350,  0.7566,  0.7566,  ...,  0.7566,  0.7566,  0.7350],\n",
      "         [-0.3658, -0.3658, -0.3658,  ..., -0.3658, -0.3658, -0.3658],\n",
      "         [-1.0724, -1.0724, -1.0724,  ..., -1.0724, -1.0724, -1.0724]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.1235, -1.1451, -1.1451,  ..., -1.1451, -1.1235, -1.1451],\n",
      "         [ 0.3913,  0.4274,  0.4274,  ...,  0.4274,  0.4274,  0.3913],\n",
      "         [-1.0991, -1.0991, -1.0991,  ..., -1.0991, -1.0991, -1.0991]],\n",
      "\n",
      "        [[ 0.5286,  0.7483,  1.0339,  ...,  1.0559,  0.9460,  0.7922],\n",
      "         [-0.7435, -0.9603, -0.7796,  ..., -0.7073, -0.6351, -0.7435],\n",
      "         [ 1.2955,  1.0981,  0.3932,  ...,  0.6470,  1.0981,  1.1263]],\n",
      "\n",
      "        [[-0.8655, -0.8655, -0.8655,  ..., -0.8655, -0.8655, -0.8655],\n",
      "         [-0.0064, -0.0064, -0.0064,  ..., -0.0064, -0.0064, -0.0064],\n",
      "         [-1.3600, -1.3340, -1.3600,  ..., -1.3340, -1.3340, -1.3600]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4235,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-2.1291,  2.4267],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759]])]\n",
      "第 7 行: [tensor([[[ 0.7075,  0.7295,  0.7075,  ..., -0.2152,  0.6856,  0.8174],\n",
      "         [-0.1272, -0.0549, -0.0910,  ..., -1.7174, -1.0307,  0.0174],\n",
      "         [ 1.5006,  1.5006,  1.5288,  ...,  0.9649,  1.2468,  1.3878]],\n",
      "\n",
      "        [[-1.3168, -1.4032, -1.6406,  ..., -1.5543, -1.6838, -1.5327],\n",
      "         [-0.4503, -0.2698, -0.5947,  ..., -0.1614, -0.0531, -0.1614],\n",
      "         [-0.5581, -0.6624,  0.1717,  ..., -0.0629, -0.0368, -0.1150]],\n",
      "\n",
      "        [[-0.1054, -0.1054, -0.0614,  ...,  0.1363,  0.0924, -0.0614],\n",
      "         [-2.0426, -2.0426, -2.0426,  ..., -2.0065, -2.0065, -2.0065],\n",
      "         [ 0.1472,  0.1472,  0.1754,  ...,  0.0626,  0.1472,  0.0908]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.3896, -0.4111, -0.4111,  ..., -0.3896, -0.3896, -0.4111],\n",
      "         [-0.4031, -0.3669, -0.3669,  ..., -0.3669, -0.3669, -0.3669],\n",
      "         [-1.4900, -1.4900, -1.4900,  ..., -1.4900, -1.4900, -1.4900]],\n",
      "\n",
      "        [[-1.4453, -1.4669, -1.4453,  ..., -1.5306, -1.5101, -1.5317],\n",
      "         [-0.0526, -0.0526, -0.0887,  ...,  0.2730,  0.0919,  0.1280],\n",
      "         [-0.5839, -0.5839, -0.5578,  ...,  0.0159, -0.3753, -0.2971]],\n",
      "\n",
      "        [[ 0.6826,  0.7924,  0.8364,  ...,  0.8583,  0.8583,  0.7924],\n",
      "         [ 0.3408,  0.1240,  0.2686,  ...,  0.2324,  0.2686,  0.2324],\n",
      "         [ 1.5780,  1.4934,  1.4088,  ...,  1.3806,  1.3806,  1.4370]]]), tensor([[ 0.9593, -0.1164],\n",
      "        [ 1.3813, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164]])]\n",
      "第 8 行: [tensor([[[ 1.0563,  1.0563,  1.0563,  ...,  1.0563,  1.0563,  1.0563],\n",
      "         [ 1.3168,  1.3168,  1.3168,  ...,  1.3168,  1.3168,  1.2806],\n",
      "         [ 0.5352,  0.5352,  0.5352,  ...,  0.5352,  0.5352,  0.5352]],\n",
      "\n",
      "        [[ 1.1441,  1.1441,  1.1656,  ...,  1.5974,  1.4895,  1.0793],\n",
      "         [-0.2454, -0.2454, -0.2093,  ..., -0.2454,  0.1157,  0.1518],\n",
      "         [ 0.0347,  0.0607,  0.0607,  ...,  0.2171, -0.0175,  0.3214]],\n",
      "\n",
      "        [[ 0.9519,  0.9735,  0.9519,  ...,  0.9735,  0.9519,  0.9735],\n",
      "         [ 0.0680,  0.0680,  0.0680,  ...,  0.0680,  0.0680,  0.0680],\n",
      "         [-0.8896, -0.8635, -0.8635,  ..., -0.8635, -0.8635, -0.8635]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.9693,  0.9912,  0.9912,  ...,  0.9912,  1.0352,  0.9912],\n",
      "         [-0.0199, -0.0199, -0.0199,  ...,  0.0162, -0.0199,  0.0524],\n",
      "         [ 1.1576,  1.1576,  1.1012,  ...,  1.1576,  1.1012,  1.1294]],\n",
      "\n",
      "        [[ 1.0593,  1.0593,  1.0593,  ...,  0.3562,  0.1805,  0.0047],\n",
      "         [-0.4162, -0.4523, -0.4523,  ..., -1.5365, -1.7172, -1.7534],\n",
      "         [ 0.8526,  0.8526,  0.8526,  ...,  0.7962,  0.9936,  0.9936]],\n",
      "\n",
      "        [[ 0.8501,  0.8501,  0.8501,  ...,  0.8501,  0.8501,  0.8512],\n",
      "         [-0.7708, -0.7708, -0.7708,  ..., -0.7708, -0.7708, -0.7703],\n",
      "         [ 0.8780,  0.8780,  0.8780,  ...,  0.8780,  0.8780,  0.9044]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [-2.4235,  0.9008],\n",
      "        [ 1.1243,  6.4957],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.2204, -0.1164]])]\n",
      "第 9 行: [tensor([[[ 1.1894,  1.1894,  1.1894,  ...,  1.1894,  1.2110,  1.1894],\n",
      "         [ 0.5013,  0.5013,  0.5013,  ...,  0.5013,  0.5013,  0.5013],\n",
      "         [-0.2380, -0.2380, -0.2380,  ..., -0.2119, -0.2119, -0.2119]],\n",
      "\n",
      "        [[ 1.1860,  1.1860,  1.1860,  ...,  1.1860,  1.1860,  1.1860],\n",
      "         [-0.3108, -0.3108, -0.3108,  ..., -0.3108, -0.3108, -0.3108],\n",
      "         [ 0.4455,  0.4737,  0.4455,  ...,  0.4737,  0.4455,  0.4737]],\n",
      "\n",
      "        [[ 0.5949,  1.1881,  0.5290,  ...,  1.0123,  0.9684,  0.9464],\n",
      "         [-0.9962, -0.8155, -1.0324,  ..., -0.8155, -0.7794, -0.8878],\n",
      "         [ 1.2119,  0.4507,  1.2683,  ...,  0.6198,  0.6762,  0.7890]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.1727,  0.1727,  0.1727,  ...,  0.1511,  0.1511,  0.1727],\n",
      "         [ 0.3441,  0.3080,  0.3441,  ...,  0.3441,  0.3441,  0.3441],\n",
      "         [-1.5225, -1.5225, -1.5225,  ..., -1.5225, -1.5485, -1.5225]],\n",
      "\n",
      "        [[ 0.5037,  0.5253,  0.5469,  ...,  0.5469,  0.5469,  0.5469],\n",
      "         [ 0.6368,  0.6007,  0.6007,  ...,  0.6007,  0.6368,  0.6007],\n",
      "         [ 1.4771,  1.4511,  1.3989,  ...,  1.4511,  1.4250,  1.4250]],\n",
      "\n",
      "        [[ 0.2656,  0.5731,  0.6830,  ...,  0.5731,  0.5731,  0.4413],\n",
      "         [ 0.6302,  0.3411,  0.5941,  ...,  0.3772,  0.3411,  0.4134],\n",
      "         [ 1.7200,  1.6072,  1.4380,  ...,  1.6354,  1.6354,  1.7200]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 1.3842,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4235,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9405, -0.3199]])]\n",
      "第 10 行: [tensor([[[-2.7240e-01, -2.7240e-01, -2.7240e-01,  ..., -6.1779e-01,\n",
      "           8.2854e-01,  7.1966e-02],\n",
      "         [-1.1680e+00, -1.1680e+00, -1.1680e+00,  ..., -1.2041e+00,\n",
      "          -3.7363e-01, -4.8251e-01],\n",
      "         [ 1.2950e+00,  1.2950e+00,  1.3211e+00,  ...,  1.0865e+00,\n",
      "           1.5817e+00,  6.9520e-01]],\n",
      "\n",
      "        [[ 9.7352e-01,  9.7352e-01,  9.7352e-01,  ...,  9.7352e-01,\n",
      "           9.7352e-01,  9.7352e-01],\n",
      "         [ 6.8029e-02,  6.8029e-02,  6.8029e-02,  ...,  6.8029e-02,\n",
      "           6.8029e-02,  6.8029e-02],\n",
      "         [-8.6354e-01, -8.6354e-01, -8.6354e-01,  ..., -8.8961e-01,\n",
      "          -8.6354e-01, -8.6354e-01]],\n",
      "\n",
      "        [[-1.5597e+00, -1.5597e+00, -1.5597e+00,  ..., -1.5597e+00,\n",
      "          -1.5597e+00, -1.5597e+00],\n",
      "         [-5.7464e-02, -5.7464e-02, -5.7464e-02,  ..., -2.1323e-02,\n",
      "          -5.7464e-02, -5.7464e-02],\n",
      "         [ 1.6510e-01,  1.6510e-01,  1.6510e-01,  ...,  1.6510e-01,\n",
      "           1.9330e-01,  1.6510e-01]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 4.1144e-03,  2.5701e-02,  4.7289e-02,  ...,  5.1446e-03,\n",
      "           2.6732e-02,  2.6732e-02],\n",
      "         [-1.8924e+00, -1.8924e+00, -1.8924e+00,  ..., -1.8918e+00,\n",
      "          -1.8918e+00, -1.8918e+00],\n",
      "         [-5.0471e-01, -4.7864e-01, -4.7864e-01,  ..., -5.0439e-01,\n",
      "          -4.7832e-01, -4.7832e-01]],\n",
      "\n",
      "        [[-6.3741e-01, -3.0767e+00, -6.6170e+00,  ..., -1.0918e+00,\n",
      "          -4.2651e+00, -5.7114e+00],\n",
      "         [-3.6818e+00, -1.2264e+00,  4.3704e+00,  ..., -3.2851e+00,\n",
      "          -3.5401e-02,  7.0418e+00],\n",
      "         [ 4.7425e-01, -9.8534e-01, -2.1061e+00,  ...,  5.7819e-01,\n",
      "          -8.5534e-01, -6.2076e-01]],\n",
      "\n",
      "        [[ 2.2248e-01,  3.7627e-01,  3.1036e-01,  ...,  2.4445e-01,\n",
      "           2.6642e-01,  3.3233e-01],\n",
      "         [ 2.0041e+00,  2.1486e+00,  2.1125e+00,  ...,  2.2209e+00,\n",
      "           2.2209e+00,  2.1486e+00],\n",
      "         [ 8.7616e-01,  9.8894e-01,  9.0435e-01,  ...,  8.7616e-01,\n",
      "           9.0435e-01,  9.8894e-01]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-2.4235,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4235,  0.9008],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2131, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-2.1291,  2.4267],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4235,  0.9008],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [-2.4235,  0.9008]])]\n",
      "\n",
      "test_loader 前 10 行数据：\n",
      "第 1 行: [tensor([[[ 1.1655,  1.1655,  1.1655,  ...,  1.1655,  1.1655,  1.1655],\n",
      "         [ 0.9911,  0.9911,  0.9911,  ...,  0.9911,  0.9911,  0.9911],\n",
      "         [ 0.3363,  0.3363,  0.3081,  ...,  0.3363,  0.3081,  0.3081]],\n",
      "\n",
      "        [[-1.5375, -1.5375, -1.5375,  ..., -1.5375, -1.5375, -1.5595],\n",
      "         [-0.0573, -0.0573, -0.0573,  ..., -0.0573, -0.0573, -0.0573],\n",
      "         [ 0.1656,  0.1656,  0.1656,  ...,  0.1656,  0.1656,  0.1656]],\n",
      "\n",
      "        [[ 1.0126,  1.0126,  1.0126,  ...,  1.0126,  1.0126,  1.0126],\n",
      "         [-0.5841, -0.5841, -0.5480,  ..., -0.5480, -0.5841, -0.5480],\n",
      "         [-0.5781, -0.5781, -0.5781,  ..., -0.5781, -0.5781, -0.5781]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.6661, -0.7956, -0.5582,  ..., -0.6014, -0.7525, -0.4718],\n",
      "         [-1.4958, -1.1708, -1.0264,  ..., -0.9541, -1.2791, -0.8458],\n",
      "         [-1.1045, -0.8699, -1.0263,  ..., -0.7917, -0.7657, -1.0263]],\n",
      "\n",
      "        [[-1.4875, -1.4875, -1.4875,  ..., -1.4659, -1.4875, -1.4875],\n",
      "         [-0.0159,  0.0202,  0.0202,  ...,  0.0924,  0.1285,  0.1285],\n",
      "         [-0.5053, -0.5053, -0.4793,  ..., -0.5575, -0.6096, -0.5314]],\n",
      "\n",
      "        [[ 1.0375,  0.9936,  0.9496,  ...,  1.0373,  0.9494,  1.0153],\n",
      "         [-0.1992, -0.1631, -0.1631,  ..., -0.0548, -0.0909, -0.1271],\n",
      "         [ 0.9941,  1.0505,  1.0787,  ...,  1.1909,  1.1909,  1.0500]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4235,  0.9008],\n",
      "        [ 0.2248,  0.1887],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.2251,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.5877,  0.9008],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164]])]\n",
      "第 2 行: [tensor([[[ 0.5859,  0.9529,  1.1472,  ...,  0.4780,  0.3485,  0.6723],\n",
      "         [ 0.0208,  0.3819,  0.3457,  ..., -1.3513, -1.3152, -0.7014],\n",
      "         [ 1.3716,  0.7982,  0.6679,  ...,  0.6679,  0.1466,  1.0588]],\n",
      "\n",
      "        [[ 0.7052,  0.6832,  0.7052,  ...,  0.6832,  0.7052,  0.6832],\n",
      "         [ 0.6304,  0.5942,  0.6304,  ...,  0.6304,  0.6304,  0.6304],\n",
      "         [ 1.5231,  1.5231,  1.5231,  ...,  1.5231,  1.5231,  1.5231]],\n",
      "\n",
      "        [[-0.4122, -0.4122, -0.3906,  ..., -0.3906, -0.3906, -0.3906],\n",
      "         [-0.3675, -0.3675, -0.3675,  ..., -0.3675, -0.4036, -0.3675],\n",
      "         [-1.4904, -1.4904, -1.4904,  ..., -1.4904, -1.4904, -1.4904]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-0.4286, -0.4286, -0.4286,  ..., -0.4286, -0.4286, -0.4286],\n",
      "         [-0.2203, -0.2203, -0.2203,  ..., -0.2203, -0.2203, -0.2203],\n",
      "         [-1.5148, -1.5148, -1.5148,  ..., -1.5148, -1.5148, -1.5148]],\n",
      "\n",
      "        [[ 1.1688,  1.1904,  1.1904,  ...,  1.1678,  1.1894,  1.1894],\n",
      "         [ 0.8630,  0.8630,  0.8630,  ...,  0.8624,  0.8624,  0.8624],\n",
      "         [ 0.0230,  0.0230,  0.0230,  ...,  0.0487,  0.0226,  0.0226]],\n",
      "\n",
      "        [[ 1.2353,  1.2353,  1.2353,  ...,  1.2572,  1.2572,  1.2572],\n",
      "         [ 0.4152,  0.4152,  0.4152,  ...,  0.3790,  0.3429,  0.3790],\n",
      "         [ 0.3456,  0.3456,  0.3738,  ...,  0.2892,  0.2892,  0.2892]]]), tensor([[-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-2.4235,  0.9008],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164]])]\n",
      "第 3 行: [tensor([[[-0.9375, -0.9375, -0.9375,  ..., -0.9375, -0.9375, -0.9375],\n",
      "         [ 1.4701,  1.4701,  1.4701,  ...,  1.4701,  1.4701,  1.4701],\n",
      "         [ 1.0878,  1.1138,  1.0878,  ...,  1.1138,  1.0878,  1.0878]],\n",
      "\n",
      "        [[-1.4237, -1.4227, -1.4227,  ..., -1.4227, -1.4227, -1.4227],\n",
      "         [ 0.5974,  0.5979,  0.5979,  ...,  0.5979,  0.5979,  0.5979],\n",
      "         [-0.6620, -0.6617, -0.6617,  ..., -0.6617, -0.6617, -0.6617]],\n",
      "\n",
      "        [[ 0.6919,  0.6919,  0.6919,  ...,  0.6919,  0.6919,  0.6919],\n",
      "         [-0.3297, -0.3297, -0.3658,  ..., -0.3297, -0.3297, -0.3297],\n",
      "         [-1.1245, -1.1245, -1.1506,  ..., -1.1506, -1.1506, -1.1506]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.0563,  1.0343,  1.1002,  ...,  0.7707,  0.8146,  0.7707],\n",
      "         [ 0.2687,  0.3048,  0.2687,  ...,  0.3048,  0.2326,  0.2687],\n",
      "         [ 1.0428,  1.0710,  1.1273,  ...,  1.5221,  1.4657,  1.4657]],\n",
      "\n",
      "        [[ 0.4439,  0.5977,  0.8833,  ...,  0.0265,  0.0704,  0.0265],\n",
      "         [-1.6451,  1.3184,  1.4991,  ...,  1.9328,  1.9690,  1.9328],\n",
      "         [ 0.5419,  1.9517,  1.3314,  ...,  1.3596,  1.3032,  1.3032]],\n",
      "\n",
      "        [[-1.1801, -1.7414, -0.1008,  ..., -1.5471, -1.5255, -1.5255],\n",
      "         [ 1.4556,  0.3363,  0.3363,  ...,  0.1918,  0.1918,  0.2279],\n",
      "         [-0.8232,  0.6103, -1.6573,  ...,  0.4539,  0.4800,  0.5061]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.2516,  0.9008],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-2.4238, -0.3199],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-2.4235,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 1.1243,  6.4957],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9463,  0.6974]])]\n",
      "第 4 行: [tensor([[[-1.5373, -1.5373, -1.5373,  ..., -1.5373, -1.5373, -1.5373],\n",
      "         [ 0.0151,  0.0151,  0.0151,  ...,  0.0151,  0.0512,  0.0512],\n",
      "         [-0.1158, -0.1158, -0.1158,  ..., -0.1158, -0.1158, -0.1158]],\n",
      "\n",
      "        [[ 0.8579,  0.8579,  0.8359,  ...,  0.8579,  0.8579,  0.8579],\n",
      "         [-0.1292, -0.0931, -0.1292,  ..., -0.1292, -0.1292, -0.1292],\n",
      "         [ 1.3514,  1.3232,  1.3514,  ...,  1.3514,  1.3232,  1.3514]],\n",
      "\n",
      "        [[-1.5090, -1.4875, -1.4875,  ..., -1.4659, -1.4659, -1.4875],\n",
      "         [ 0.0202, -0.0159,  0.0202,  ...,  0.1285,  0.0924,  0.1285],\n",
      "         [-0.5835, -0.5053, -0.5053,  ..., -0.6096, -0.5575, -0.6096]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.1738,  0.1738,  0.1738,  ...,  0.1738,  0.1738,  0.1738],\n",
      "         [-0.1853, -0.1853, -0.1853,  ..., -0.1853, -0.1853, -0.2214],\n",
      "         [-1.4894, -1.4894, -1.4894,  ..., -1.4894, -1.4894, -1.4894]],\n",
      "\n",
      "        [[-1.4864, -1.4864, -1.4864,  ..., -1.4648, -1.4864, -1.4864],\n",
      "         [-0.0514, -0.0514, -0.1598,  ..., -0.1598, -0.1598, -0.1598],\n",
      "         [-0.4790, -0.4790, -0.4790,  ..., -0.5311, -0.5050, -0.4529]],\n",
      "\n",
      "        [[ 1.0793,  1.0793,  1.0793,  ...,  1.0795,  1.0795,  1.0795],\n",
      "         [ 1.1006,  1.1006,  1.1006,  ...,  1.1007,  1.1007,  1.1007],\n",
      "         [ 0.6788,  0.6788,  0.6788,  ...,  0.7075,  0.6793,  0.6793]]]), tensor([[-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-2.1291,  2.4267],\n",
      "        [-2.4238, -0.3199],\n",
      "        [ 1.3842,  0.3922],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.1291,  2.4267],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [ 1.1243,  6.4957],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4238, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759]])]\n",
      "第 5 行: [tensor([[[ 2.5979,  1.5872, -0.9833,  ..., -0.4558, -0.6975, -0.4338],\n",
      "         [ 0.5961,  1.2105,  2.8007,  ...,  2.2226,  2.1503,  2.1142],\n",
      "         [ 1.8692,  2.6022,  0.9387,  ...,  0.8264,  0.7701,  1.1366]],\n",
      "\n",
      "        [[-1.2459, -1.3538, -1.7424,  ..., -1.3117, -1.5275, -1.3333],\n",
      "         [-0.3025,  0.4557,  0.8890,  ..., -0.7725, -0.1948, -0.1587],\n",
      "         [ 0.2518,  1.0598,  0.9555,  ...,  0.3297,  0.5903,  0.1212]],\n",
      "\n",
      "        [[-1.4217, -1.4217, -1.4648,  ..., -1.4432, -1.4422, -1.4422],\n",
      "         [-0.6653, -0.5931, -0.5931,  ..., -0.5570, -0.5564, -0.5564],\n",
      "         [-0.2183, -0.2704, -0.2704,  ..., -0.2704, -0.2701, -0.2701]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.3836, -1.3836, -1.3836,  ..., -1.3836, -1.4052, -1.3836],\n",
      "         [ 0.1019,  0.1019,  0.1019,  ...,  0.1019,  0.1019,  0.1019],\n",
      "         [-0.6824, -0.7084, -0.7084,  ..., -0.7084, -0.7084, -0.7084]],\n",
      "\n",
      "        [[ 0.9735,  0.9735,  0.9735,  ...,  0.9735,  0.9735,  0.9735],\n",
      "         [ 0.0680,  0.0680,  0.0680,  ...,  0.0680,  0.0680,  0.0680],\n",
      "         [-0.8635, -0.8635, -0.8635,  ..., -0.8635, -0.8635, -0.8635]],\n",
      "\n",
      "        [[ 0.6615,  0.4198,  0.5296,  ...,  0.5077,  0.5736,  0.6615],\n",
      "         [-0.2007, -0.0562,  0.0161,  ...,  0.2691,  0.3052,  0.2691],\n",
      "         [ 1.5800,  1.7210,  1.6928,  ...,  1.6082,  1.6928,  1.5236]]]), tensor([[-2.1291,  2.4267],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.4238, -0.3199],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2131, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2131, -0.3199]])]\n",
      "第 6 行: [tensor([[[ 1.0126,  1.0126,  1.0126,  ...,  1.0126,  1.0126,  1.0126],\n",
      "         [-0.5480, -0.5480, -0.5841,  ..., -0.5841, -0.5841, -0.5480],\n",
      "         [-0.5781, -0.5781, -0.5781,  ..., -0.5781, -0.5781, -0.5520]],\n",
      "\n",
      "        [[ 1.1655,  1.1655,  1.1655,  ...,  1.1655,  1.1655,  1.1655],\n",
      "         [ 0.9911,  0.9911,  0.9911,  ...,  0.9911,  0.9911,  0.9911],\n",
      "         [ 0.3363,  0.3081,  0.3363,  ...,  0.3081,  0.3081,  0.3363]],\n",
      "\n",
      "        [[ 1.4326,  1.1909,  1.1470,  ...,  1.0591,  1.0151,  0.9272],\n",
      "         [-0.9223, -0.3079, -0.2356,  ..., -0.8861, -0.9945, -1.1391],\n",
      "         [ 0.3445, -0.2758, -0.3321,  ...,  0.1754,  0.2036,  0.1190]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.5286, -1.5286, -1.5286,  ..., -1.5286, -1.5286, -1.5286],\n",
      "         [-0.0509, -0.0509,  0.0574,  ..., -0.1592, -0.1592, -0.1592],\n",
      "         [-0.1659, -0.1659, -0.1659,  ..., -0.0095,  0.0166,  0.0166]],\n",
      "\n",
      "        [[ 0.3937,  0.9334,  0.9992,  ...,  0.8039,  0.6743,  0.6528],\n",
      "         [-1.4224, -0.6281, -0.3747,  ...,  0.4191,  0.2024,  0.3108],\n",
      "         [-0.8953, -0.7129, -0.7386,  ...,  1.0595,  1.2419,  1.4765]],\n",
      "\n",
      "        [[ 1.1452,  1.1452,  1.1452,  ...,  1.1452,  1.1452,  1.1452],\n",
      "         [-0.3658, -0.3658, -0.3658,  ..., -0.3658, -0.3658, -0.3658],\n",
      "         [ 0.3351,  0.3351,  0.3351,  ...,  0.3351,  0.3351,  0.3351]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2131, -0.3199],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.1243,  6.4957],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-2.4235,  0.9008],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759]])]\n",
      "第 7 行: [tensor([[[-3.8038e-01, -4.6673e-01, -1.4292e-01,  ..., -1.2017e+00,\n",
      "          -2.3030e-01, -6.8363e-01],\n",
      "         [-1.0714e+00, -3.4923e-01, -7.8253e-01,  ..., -1.5414e+00,\n",
      "          -1.7580e+00, -1.2525e+00],\n",
      "         [-9.0109e-01, -5.8832e-01, -3.5374e-01,  ..., -1.5269e+00,\n",
      "          -1.9961e+00, -8.7534e-01]],\n",
      "\n",
      "        [[ 2.8539e-01,  3.0736e-01,  3.0736e-01,  ...,  2.8539e-01,\n",
      "           2.8539e-01,  2.8539e-01],\n",
      "         [-1.1781e+00, -1.1781e+00, -1.2142e+00,  ..., -1.1781e+00,\n",
      "          -1.1781e+00, -1.1781e+00],\n",
      "         [ 1.4047e+00,  1.4047e+00,  1.4047e+00,  ...,  1.4047e+00,\n",
      "           1.4047e+00,  1.4047e+00]],\n",
      "\n",
      "        [[-9.8911e-01, -9.8911e-01, -9.8911e-01,  ..., -9.8911e-01,\n",
      "          -9.8911e-01, -9.8911e-01],\n",
      "         [-2.3855e-01, -2.7469e-01, -2.7469e-01,  ..., -2.7469e-01,\n",
      "          -2.3855e-01, -2.3855e-01],\n",
      "         [-1.1898e+00, -1.1898e+00, -1.1898e+00,  ..., -1.1898e+00,\n",
      "          -1.1898e+00, -1.1898e+00]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 3.7584e-01,  3.9781e-01,  3.7606e-01,  ...,  4.1978e-01,\n",
      "           5.0767e-01,  5.0767e-01],\n",
      "         [ 1.2452e-01,  1.2452e-01,  1.2465e-01,  ...,  2.6908e-01,\n",
      "           3.4136e-01,  3.4136e-01],\n",
      "         [ 1.7774e+00,  1.7774e+00,  1.7779e+00,  ...,  1.6928e+00,\n",
      "           1.6928e+00,  1.7210e+00]],\n",
      "\n",
      "        [[-1.1164e+00, -1.1596e+00, -1.3549e+00,  ..., -5.7775e-01,\n",
      "          -4.9141e-01, -4.2664e-01],\n",
      "         [ 8.2957e-02,  6.2458e-01,  2.9905e-01,  ...,  4.7959e-01,\n",
      "          -1.3425e-01,  3.3516e-01],\n",
      "         [-1.1624e+00, -1.3448e+00, -1.1106e+00,  ..., -1.5276e+00,\n",
      "          -1.5015e+00, -1.5536e+00]],\n",
      "\n",
      "        [[-1.4341e+00, -3.3318e-01, -1.3478e+00,  ..., -4.6373e-01,\n",
      "          -1.2840e+00, -4.6373e-01],\n",
      "         [ 2.4460e+00,  2.4099e+00,  2.3738e+00,  ...,  2.4094e+00,\n",
      "           2.7705e+00,  3.1315e+00],\n",
      "         [-4.9618e-01, -4.4405e-01, -8.6108e-01,  ...,  2.4784e-02,\n",
      "          -4.7043e-01, -1.2806e-03]]]), tensor([[ 2.6137,  1.4094],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2204, -0.1164],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2131, -0.3199],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 2.6137,  1.4094]])]\n",
      "第 8 行: [tensor([[[-3.0274, -0.8471, -1.9491,  ..., -1.3446, -3.1579, -0.3948],\n",
      "         [ 0.5101, -2.0175, -1.0070,  ..., -2.0180,  0.9429,  2.5316],\n",
      "         [-0.9574,  0.1634, -1.0619,  ...,  0.4758, -0.9056, -0.6188]],\n",
      "\n",
      "        [[ 0.1365,  0.1146,  0.2244,  ...,  0.4661,  0.6199,  0.5759],\n",
      "         [-1.9702, -1.9702, -1.9341,  ..., -1.7172, -1.6449, -1.6811],\n",
      "         [ 0.3451,  0.3451,  0.3451,  ...,  0.2605,  0.1759,  0.2887]],\n",
      "\n",
      "        [[ 0.7058,  0.7058,  0.7058,  ...,  0.7058,  0.7058,  0.7058],\n",
      "         [-0.0559, -0.0198, -0.0198,  ..., -0.0198, -0.0198, -0.0198],\n",
      "         [ 1.5247,  1.5247,  1.5247,  ...,  1.5528,  1.5247,  1.5247]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.3631, -1.4063, -1.1904,  ..., -1.0177, -1.3631, -1.1041],\n",
      "         [ 0.1502, -0.0304, -0.4637,  ...,  0.3307,  1.1612,  1.7750],\n",
      "         [-0.9828, -0.8785, -1.1392,  ..., -1.3998, -0.8785, -0.6961]],\n",
      "\n",
      "        [[ 0.7935,  0.3321,  0.6617,  ...,  0.3101,  0.3541,  0.3541],\n",
      "         [ 0.4138,  2.3653,  2.2931,  ...,  2.3653,  2.4015,  2.4015],\n",
      "         [ 0.9320,  0.9038,  0.5373,  ...,  0.3963,  0.3963,  0.3963]],\n",
      "\n",
      "        [[ 0.0058, -0.1260, -0.0821,  ...,  0.4672,  0.2914,  0.4672],\n",
      "         [ 1.6445,  2.2588,  1.7529,  ...,  2.3311,  2.3311,  2.8371],\n",
      "         [-0.7238, -0.8084, -1.2031,  ...,  0.7424,  0.2067,  0.4322]]]), tensor([[ 2.6137,  1.4094],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.9405, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2251,  0.9008],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-2.4235,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 2.5845,  0.3922],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 2.5845,  0.3922]])]\n",
      "第 9 行: [tensor([[[ 0.0288, -0.0360,  0.0720,  ...,  0.0936,  0.1151,  0.0936],\n",
      "         [-0.1214, -0.1936, -0.1214,  ..., -0.1936, -0.1936, -0.1936],\n",
      "         [ 1.6596,  1.6335,  1.6335,  ...,  1.6074,  1.6074,  1.6074]],\n",
      "\n",
      "        [[-3.6751, -0.7824, -2.2061,  ..., -0.6951, -1.5801, -3.0049],\n",
      "         [ 0.1256, -2.7991, -1.4626,  ..., -1.8237, -2.0042,  1.2094],\n",
      "         [-1.5963, -0.0324, -0.6316,  ..., -0.1624, -0.7880, -0.9444]],\n",
      "\n",
      "        [[ 0.5339,  0.6436,  0.6216,  ...,  0.7314,  0.2920,  0.2481],\n",
      "         [ 2.1148,  2.5484,  2.5484,  ...,  2.6206,  2.7291,  2.2954],\n",
      "         [ 0.8854,  0.2646,  0.5183,  ...,  0.3492,  0.2646,  0.2928]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 0.4471,  0.1449, -0.8697,  ..., -1.3878, -1.3878, -1.3878],\n",
      "         [-1.0314, -1.7174, -1.6091,  ...,  0.2324,  0.2324,  0.2324],\n",
      "         [ 2.1506,  0.4304, -0.4558,  ..., -0.7946, -0.7685, -0.7946]],\n",
      "\n",
      "        [[ 1.1996,  1.2212,  1.2212,  ...,  1.2223,  1.2223,  1.2212],\n",
      "         [ 0.2063,  0.1702,  0.1702,  ...,  0.2069,  0.1708,  0.2063],\n",
      "         [-0.0591, -0.0330, -0.0330,  ..., -0.0587, -0.0587, -0.0591]],\n",
      "\n",
      "        [[ 0.0056, -0.3679,  0.9723,  ...,  0.5770,  0.6209,  1.5437],\n",
      "         [ 0.9215,  1.0299,  2.2949,  ...,  0.7048,  1.6806,  1.6445],\n",
      "         [ 0.8546,  0.9110,  1.7287,  ...,  1.4473,  1.5882,  2.2085]]]), tensor([[ 0.2219, -0.3199],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-2.4238, -0.3199],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9405, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9463,  0.6974],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 1.0194,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.6137,  1.4094],\n",
      "        [-0.9536,  1.7146],\n",
      "        [ 1.3813, -0.1164],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.2516,  0.9008],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 1.3842,  0.3922],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-2.1291,  2.4267]])]\n",
      "第 10 行: [tensor([[[-0.4122, -0.4122, -0.4122,  ..., -0.3906, -0.4122, -0.4122],\n",
      "         [-0.3675, -0.3675, -0.3675,  ..., -0.3675, -0.3675, -0.3675],\n",
      "         [-1.4904, -1.4904, -1.4904,  ..., -1.4904, -1.4904, -1.4904]],\n",
      "\n",
      "        [[-0.3875, -0.3865, -0.3865,  ..., -0.3649, -0.3875, -0.3659],\n",
      "         [-0.2575, -0.2569, -0.2569,  ..., -0.2569, -0.2575, -0.2575],\n",
      "         [-1.5155, -1.5151, -1.5151,  ..., -1.5151, -1.4894, -1.4894]],\n",
      "\n",
      "        [[-1.0609, -0.8676, -0.1552,  ..., -0.7165, -0.1121, -0.2632],\n",
      "         [ 1.4979, -0.3081,  0.3780,  ...,  1.9306, -0.4886,  0.5946],\n",
      "         [-1.0282, -1.6801, -1.5759,  ..., -1.6541, -1.5498, -1.5238]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.0795,  1.0793,  1.0793,  ...,  1.0574,  1.0793,  1.0793],\n",
      "         [ 1.1007,  1.1006,  1.1006,  ...,  1.1367,  1.1006,  1.1006],\n",
      "         [ 0.7075,  0.6788,  0.6788,  ...,  0.6788,  0.6788,  0.7070]],\n",
      "\n",
      "        [[ 0.9715,  0.9715,  0.9715,  ...,  0.9715,  0.9930,  0.9715],\n",
      "         [ 0.1391,  0.1391,  0.1752,  ...,  0.1391,  0.1752,  0.1391],\n",
      "         [-0.8381, -0.8381, -0.8381,  ..., -0.8381, -0.8642, -0.8381]],\n",
      "\n",
      "        [[ 1.1431,  1.1431,  1.1421,  ...,  1.1421,  1.1421,  1.1421],\n",
      "         [-0.3669, -0.3669, -0.3675,  ..., -0.3675, -0.3675, -0.3675],\n",
      "         [ 0.3084,  0.3084,  0.2820,  ...,  0.3081,  0.2820,  0.3081]]]), tensor([[-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.9536,  1.7146],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 2.2516,  0.9008],\n",
      "        [-2.4238, -0.3199],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [ 0.2251,  0.9008],\n",
      "        [-0.9463,  0.6974],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9578, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2189,  0.1887],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.9593, -0.1164],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [ 0.2219, -0.3199],\n",
      "        [ 1.3813, -0.1164],\n",
      "        [-2.1291,  2.4267],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759],\n",
      "        [-0.3710, -0.6759]])]\n",
      "\n",
      "train_loader 数据行数: 14708\n",
      "test_loader 数据行数: 3678\n",
      "训练集前十个样本：\n",
      "样本 1:\n",
      "输入数据: tensor([[ 0.0704,  0.2901,  0.5098,  0.4000,  0.6197,  1.2568,  1.2568,  1.1470,\n",
      "          1.3886,  0.9053,  1.0591,  1.1250,  1.1250,  0.9272,  1.0371,  0.0265,\n",
      "          1.2788,  0.0924,  2.1796,  0.4878],\n",
      "        [ 1.0293, -1.1030,  0.1619,  1.4269,  0.1981, -0.4163, -0.0549, -0.3440,\n",
      "         -0.2717, -0.8138,  0.0535,  0.0174, -0.2356, -0.0188,  0.5956, -1.6812,\n",
      "          0.5595, -0.8138,  1.9690,  2.3665],\n",
      "        [ 2.0645,  0.4009,  1.8671,  2.0645,  1.3314,  1.1340, -0.3603,  0.3164,\n",
      "         -0.1912,  0.8521,  1.1622,  1.0776,  0.4573,  1.2186,  1.6697,  0.4855,\n",
      "          1.1904,  1.6697,  1.5288,  0.2600]])\n",
      "目标数据: tensor([ 0.9593, -0.1164])\n",
      "样本 2:\n",
      "输入数据: tensor([[ 0.6244,  1.9426,  0.0532,  1.6130,  1.3055,  0.0092,  2.5578,  1.2615,\n",
      "         -0.0347,  2.5578,  0.4926,  0.4486,  2.8434,  0.0312,  2.5797,  1.9206,\n",
      "         -0.2544,  3.3926,  1.4153,  0.0532],\n",
      "        [ 2.3693,  1.9356, -1.7145,  1.0683,  0.1648, -1.3531,  1.3935,  0.8153,\n",
      "         -1.6061,  0.8153,  0.8514, -1.5338, -0.3051,  1.7188, -1.4977, -0.8472,\n",
      "          2.2248, -0.3773, -0.5942,  2.2248],\n",
      "        [-0.4618,  0.0175, -0.0389, -0.7156,  0.3558, -0.2645,  0.3558,  0.0457,\n",
      "          0.1303,  0.4122,  0.0175, -0.1235, -0.0953, -0.3208, -0.9411, -0.1517,\n",
      "         -0.5182, -0.3208,  0.0457, -0.5746]])\n",
      "目标数据: tensor([1.1243, 6.4957])\n",
      "样本 3:\n",
      "输入数据: tensor([[ 0.1788,  0.1788,  0.1788,  0.1788,  0.1568,  0.1788,  0.1788,  0.2007,\n",
      "          0.1348, -0.1288,  0.0469, -0.0190, -0.0190,  0.0250,  0.0030,  0.0030,\n",
      "         -0.0409,  0.0689,  0.1348,  0.4424],\n",
      "        [ 2.4379,  2.4379,  2.4379,  2.4379,  2.4379,  2.4379,  2.4379,  2.4017,\n",
      "          2.4379,  2.5102,  2.4379,  2.4379,  2.4379,  2.3295,  2.2933,  2.2933,\n",
      "          2.3295,  2.3656,  2.3295,  2.4740],\n",
      "        [ 0.6229,  0.6229,  0.6229,  0.6229,  0.5947,  0.5947,  0.5665,  0.5947,\n",
      "          0.7357,  0.6229,  0.6793,  0.7921,  0.8203,  0.8485,  0.9049,  0.9895,\n",
      "          0.8767,  0.8485,  0.8485,  1.0740]])\n",
      "目标数据: tensor([-2.4235,  0.9008])\n",
      "样本 4:\n",
      "输入数据: tensor([[ 1.1523,  1.1523,  1.0660,  0.9149,  0.6990,  1.1955,  1.1739,  1.0012,\n",
      "          1.8215,  0.8070,  0.7854,  1.1308,  1.1308,  1.3682,  0.3536,  1.0660,\n",
      "          0.5047,  0.4832,  0.8285,  1.1739],\n",
      "        [ 0.6735,  0.8540,  0.6013,  0.1680, -0.0487, -0.0125,  0.2763,  0.7096,\n",
      "          1.6484,  0.9624,  1.9373,  1.4318,  0.3846,  2.4789,  2.8761,  1.1068,\n",
      "          1.2151,  1.1790,  1.4318,  0.0958],\n",
      "        [ 0.4870,  0.1742,  0.6955,  0.4609,  1.1907, -0.9205, -0.6598, -0.3210,\n",
      "          0.4870, -0.1907, -0.1646,  0.1742,  0.8780, -0.9205,  0.5913,  0.9040,\n",
      "          1.5296,  1.4514,  0.9822, -0.5295]])\n",
      "目标数据: tensor([1.3842, 0.3922])\n",
      "样本 5:\n",
      "输入数据: tensor([[-0.5870, -0.5438, -0.7597, -0.7381, -0.6302, -1.1267, -1.0835, -1.2562,\n",
      "         -1.1698, -1.0835, -0.9108, -0.9540, -1.1914, -0.8676, -1.0835, -0.7381,\n",
      "         -0.9108, -0.7597, -0.8460, -0.8244],\n",
      "        [-0.3081, -0.1276, -0.1276, -0.0915, -0.3442, -0.4525,  0.0169, -0.1637,\n",
      "          0.0169,  0.4502,  0.1974,  0.1974, -0.0553,  0.1613, -0.2720,  0.1252,\n",
      "         -0.1637,  0.1974,  0.3418,  1.3529],\n",
      "        [-1.4716, -1.5498, -1.5238, -1.6280, -1.2371, -1.1067, -1.3152, -0.8461,\n",
      "         -1.1849, -1.3934, -1.1328, -1.4977, -1.2371, -1.1067, -0.8461, -1.1067,\n",
      "         -0.9503, -1.1849, -1.1589, -1.2371]])\n",
      "目标数据: tensor([2.5877, 0.9008])\n",
      "样本 6:\n",
      "输入数据: tensor([[0.9494, 0.9494, 0.9494, 0.9714, 0.9494, 0.9934, 0.9714, 0.9714, 0.9494,\n",
      "         0.9494, 0.7956, 0.9934, 0.9494, 0.9714, 0.9934, 0.9494, 0.9494, 0.9494,\n",
      "         0.9494, 0.9494],\n",
      "        [0.2343, 0.2343, 0.2705, 0.2343, 0.2343, 0.1259, 0.2705, 0.2343, 0.2705,\n",
      "         0.2705, 0.2705, 0.2705, 0.2343, 0.2343, 0.2343, 0.2343, 0.2343, 0.2705,\n",
      "         0.2705, 0.2343],\n",
      "        [1.2191, 1.2191, 1.2191, 1.2191, 1.2191, 1.1627, 1.1909, 1.2191, 1.2191,\n",
      "         1.2473, 1.3319, 1.2191, 1.2191, 1.2191, 1.2191, 1.2191, 1.2473, 1.2473,\n",
      "         1.2191, 1.2473]])\n",
      "目标数据: tensor([ 0.9593, -0.1164])\n",
      "样本 7:\n",
      "输入数据: tensor([[ 0.0740,  0.0740,  0.0740,  0.0740,  0.0740,  0.0740,  0.0740,  0.0740,\n",
      "          0.0740,  0.0740,  0.0740,  0.0740,  0.0740,  0.0740,  0.0751,  0.0751,\n",
      "          0.0751,  0.0751,  0.0751,  0.0740],\n",
      "        [-1.9257, -1.9257, -1.9257, -1.9257, -1.9257, -1.9257, -1.9257, -1.9257,\n",
      "         -1.9257, -1.9257, -1.9257, -1.9257, -1.9257, -1.9257, -1.9252, -1.9252,\n",
      "         -1.9252, -1.9252, -1.9252, -1.9257],\n",
      "        [-0.3728, -0.3728, -0.3467, -0.3467, -0.3728, -0.3728, -0.3728, -0.3728,\n",
      "         -0.3467, -0.3467, -0.3728, -0.3728, -0.3728, -0.3467, -0.3464, -0.3725,\n",
      "         -0.3725, -0.3725, -0.3725, -0.3467]])\n",
      "目标数据: tensor([ 0.2219, -0.3199])\n",
      "样本 8:\n",
      "输入数据: tensor([[-0.4286, -0.4286, -0.4286, -0.4286, -0.4286, -0.4286, -0.4286, -0.4286,\n",
      "         -0.4286, -0.4286, -0.4502, -0.4286, -0.4286, -0.4502, -0.4286, -0.4286,\n",
      "         -0.4286, -0.4286, -0.4286, -0.4286],\n",
      "        [-0.2203, -0.2203, -0.2203, -0.1842, -0.2203, -0.2203, -0.1842, -0.1842,\n",
      "         -0.1842, -0.2203, -0.1842, -0.2203, -0.2203, -0.1842, -0.2203, -0.2203,\n",
      "         -0.2203, -0.1842, -0.2203, -0.2203],\n",
      "        [-1.5148, -1.4888, -1.5148, -1.5148, -1.5148, -1.5148, -1.5148, -1.5148,\n",
      "         -1.5148, -1.5148, -1.4888, -1.4888, -1.4888, -1.5148, -1.5148, -1.4888,\n",
      "         -1.5148, -1.5148, -1.5148, -1.5148]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "样本 9:\n",
      "输入数据: tensor([[-1.1596, -0.7494, -1.0516, -1.6129, -1.6345, -1.0948, -0.7278, -1.0516,\n",
      "         -1.8288, -2.0015, -1.6777, -1.5913, -1.8072, -1.4834, -1.5481, -1.5481,\n",
      "         -1.5697, -1.5481, -1.5903, -1.5471],\n",
      "        [ 1.8884,  1.8884,  1.7800,  1.1301,  0.3718, -0.0976, -0.0976,  1.4551,\n",
      "          1.3106,  0.0468,  0.8051,  0.4801,  0.5163,  0.4079,  0.4079,  0.4079,\n",
      "          0.4440,  0.4440,  0.4085,  0.4446],\n",
      "        [ 0.2451,  0.5318,  0.2190,  0.0626,  0.7403,  1.2877,  1.1573,  1.2877,\n",
      "          0.5839,  0.3233,  0.0366, -0.0416,  0.0366, -0.0416, -0.0155, -0.0155,\n",
      "         -0.0155,  0.0366,  0.0630, -0.0152]])\n",
      "目标数据: tensor([0.2189, 0.1887])\n",
      "样本 10:\n",
      "输入数据: tensor([[-1.5371, -1.5371, -1.5371, -1.5371, -1.5371, -1.5371, -1.5371, -1.5371,\n",
      "         -1.5371, -1.5371, -1.5371, -1.5371, -1.5371, -1.5371, -1.5371, -1.5371,\n",
      "         -1.5371, -1.5371, -1.5371, -1.5371],\n",
      "        [ 0.0152,  0.0152,  0.0152,  0.0152,  0.0152,  0.0152,  0.0152,  0.0152,\n",
      "          0.0152,  0.0152,  0.0152,  0.0152,  0.0152,  0.0152,  0.0152,  0.0152,\n",
      "          0.0152,  0.0152,  0.0152,  0.0152],\n",
      "        [-0.1717, -0.1717, -0.1717, -0.1717, -0.1717, -0.1717, -0.1717, -0.1717,\n",
      "         -0.1717, -0.1717, -0.1717, -0.1717, -0.1717, -0.1717, -0.1717, -0.1717,\n",
      "         -0.1717, -0.1717, -0.1717, -0.1717]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "\n",
      "测试集前十个样本：\n",
      "样本 1:\n",
      "输入数据: tensor([[1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655,\n",
      "         1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655, 1.1655,\n",
      "         1.1655, 1.1655],\n",
      "        [0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911,\n",
      "         0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911, 0.9911,\n",
      "         0.9911, 0.9911],\n",
      "        [0.3363, 0.3363, 0.3081, 0.3363, 0.3363, 0.3363, 0.3363, 0.3081, 0.3363,\n",
      "         0.3363, 0.3081, 0.3363, 0.3081, 0.3363, 0.3363, 0.3363, 0.3081, 0.3363,\n",
      "         0.3081, 0.3081]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "样本 2:\n",
      "输入数据: tensor([[-1.5375, -1.5375, -1.5375, -1.5595, -1.5375, -1.5595, -1.5595, -1.5595,\n",
      "         -1.5595, -1.5375, -1.5595, -1.5595, -1.5595, -1.5375, -1.5375, -1.5375,\n",
      "         -1.5595, -1.5375, -1.5375, -1.5595],\n",
      "        [-0.0573, -0.0573, -0.0573, -0.0212, -0.0573, -0.0573, -0.0573, -0.0573,\n",
      "         -0.0573, -0.0573, -0.0573, -0.0573, -0.0573, -0.0573, -0.0573, -0.0573,\n",
      "         -0.0573, -0.0573, -0.0573, -0.0573],\n",
      "        [ 0.1656,  0.1656,  0.1656,  0.1656,  0.1656,  0.1656,  0.1656,  0.1656,\n",
      "          0.1656,  0.1656,  0.1656,  0.1656,  0.1938,  0.1656,  0.1656,  0.1656,\n",
      "          0.1656,  0.1656,  0.1656,  0.1656]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "样本 3:\n",
      "输入数据: tensor([[ 1.0126,  1.0126,  1.0126,  1.0126,  0.9910,  1.0126,  1.0126,  1.0126,\n",
      "          1.0126,  1.0126,  1.0126,  1.0126,  1.0126,  1.0126,  1.0126,  1.0126,\n",
      "          0.9910,  1.0126,  1.0126,  1.0126],\n",
      "        [-0.5841, -0.5841, -0.5480, -0.5480, -0.5841, -0.5841, -0.5841, -0.5480,\n",
      "         -0.5480, -0.5841, -0.5841, -0.5841, -0.5841, -0.5480, -0.5480, -0.5480,\n",
      "         -0.5480, -0.5480, -0.5841, -0.5480],\n",
      "        [-0.5781, -0.5781, -0.5781, -0.5781, -0.5781, -0.5781, -0.5781, -0.5781,\n",
      "         -0.5781, -0.5781, -0.5781, -0.5781, -0.5781, -0.5781, -0.5781, -0.5781,\n",
      "         -0.5781, -0.5781, -0.5781, -0.5781]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "样本 4:\n",
      "输入数据: tensor([[-0.5140, -0.5140, -0.5140, -0.5150, -0.4934, -0.4934, -0.5150, -0.5150,\n",
      "         -0.4934, -0.4287, -0.7309, -0.6014, -0.7956, -0.6445, -0.6445, -0.5582,\n",
      "         -0.5582, -0.5798, -0.5582, -0.5366],\n",
      "        [-1.9285, -1.9646, -1.9646, -1.9652, -1.9652, -1.9652, -1.9652, -1.9652,\n",
      "         -1.9291, -2.0013, -1.9291, -1.8207, -1.7485, -1.8207, -1.9291, -1.9291,\n",
      "         -1.9652, -1.9291, -1.9652, -1.9652],\n",
      "        [-0.1919, -0.1919, -0.1659, -0.1662, -0.1401, -0.1401, -0.1141, -0.1401,\n",
      "          0.1205,  0.0945,  0.0423,  0.3551,  0.4854,  0.1205, -0.0359,  0.0163,\n",
      "          0.0163,  0.0163, -0.0619, -0.0359]])\n",
      "目标数据: tensor([ 0.9578, -0.1164])\n",
      "样本 5:\n",
      "输入数据: tensor([[ 1.1246,  1.0814,  0.8871,  0.9087,  0.9735,  0.7144,  0.8655,  0.9735,\n",
      "          0.8439,  1.0598,  0.9303,  0.9951,  0.8871,  0.9735,  0.4338,  0.8871,\n",
      "          0.7144,  0.6713,  0.8224,  0.8655],\n",
      "        [ 0.2008,  0.2008,  0.3091, -0.0159,  0.4174,  0.4535,  0.0924,  0.1646,\n",
      "          0.3091,  0.0563,  0.7063,  0.2730,  1.1035,  0.0924,  0.1646,  0.0924,\n",
      "          0.3091,  0.4535,  0.4174,  0.6702],\n",
      "        [-0.5835, -0.2447, -0.8442, -1.0006, -0.7920, -0.8702, -0.7660, -1.1569,\n",
      "         -0.6357, -0.7920, -0.8702, -0.6878, -1.0006, -0.8442, -0.3750, -0.6878,\n",
      "         -0.4793, -0.8702, -0.8442, -0.7660]])\n",
      "目标数据: tensor([-0.9536,  1.7146])\n",
      "样本 6:\n",
      "输入数据: tensor([[-0.9354, -0.9354, -0.9354, -0.9354, -0.9354, -0.9354, -0.9354, -0.9354,\n",
      "         -0.9354, -0.9354, -0.9354, -0.9354, -0.9354, -0.9354, -0.9354, -0.9354,\n",
      "         -0.9354, -0.9354, -0.9354, -0.9354],\n",
      "        [ 1.4712,  1.4712,  1.4712,  1.4712,  1.4712,  1.4712,  1.4712,  1.4712,\n",
      "          1.4712,  1.4712,  1.4712,  1.4712,  1.4712,  1.4712,  1.4712,  1.5073,\n",
      "          1.4712,  1.4712,  1.4712,  1.4712],\n",
      "        [ 1.0884,  1.0884,  1.0884,  1.0884,  1.0884,  1.0884,  1.0884,  1.0884,\n",
      "          1.0884,  1.0884,  1.0884,  1.0884,  1.0884,  1.0884,  1.1145,  1.0884,\n",
      "          1.0884,  1.0884,  1.0884,  1.0884]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "样本 7:\n",
      "输入数据: tensor([[ 0.7946,  0.8162,  0.7946,  0.7946,  0.7946,  0.7946,  0.7946,  0.8162,\n",
      "          0.7946,  0.7946,  0.8162,  0.7946,  0.8162,  0.7946,  0.7946,  0.8173,\n",
      "          0.7957,  0.7957,  0.7946,  0.7946],\n",
      "        [-0.3325, -0.3325, -0.3325, -0.3325, -0.3325, -0.3325, -0.3325, -0.3325,\n",
      "         -0.3325, -0.3325, -0.3325, -0.3325, -0.3325, -0.3325, -0.3325, -0.3319,\n",
      "         -0.3319, -0.3319, -0.3325, -0.3325],\n",
      "        [-1.0218, -1.0479, -1.0218, -1.0218, -1.0218, -1.0218, -1.0218, -1.0218,\n",
      "         -1.0218, -1.0479, -1.0218, -1.0218, -1.0218, -1.0218, -1.0218, -1.0215,\n",
      "         -1.0215, -1.0215, -1.0218, -1.0218]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "样本 8:\n",
      "输入数据: tensor([[-1.1657, -1.1657, -1.1657, -1.1657, -1.1657, -1.1647, -1.1647, -1.1647,\n",
      "         -1.1647, -1.1647, -1.1647, -1.1647, -1.1647, -1.1647, -1.1647, -1.1657,\n",
      "         -1.1657, -1.1431, -1.1647, -1.1647],\n",
      "        [ 1.1023,  1.1023,  1.1023,  1.1023,  1.1023,  1.1029,  1.1029,  1.1029,\n",
      "          1.1029,  1.1029,  1.1029,  1.1029,  1.1029,  1.1029,  1.1029,  1.1023,\n",
      "          1.1023,  1.1029,  1.1029,  1.1029],\n",
      "        [ 1.0057,  1.0057,  1.0057,  1.0057,  1.0057,  1.0061,  1.0061,  1.0061,\n",
      "          1.0061,  1.0061,  1.0061,  1.0061,  1.0061,  1.0061,  1.0061,  1.0057,\n",
      "          1.0057,  1.0061,  1.0061,  1.0061]])\n",
      "目标数据: tensor([-0.3710, -0.6759])\n",
      "样本 9:\n",
      "输入数据: tensor([[ 0.9695,  1.2771,  1.1892,  1.1233,  1.0354,  1.2331,  0.5520,  1.0793,\n",
      "          1.2771,  0.8157,  0.6619,  1.0134,  1.0793,  1.0574,  1.4968,  1.1672,\n",
      "          0.8157,  0.7058,  0.6839,  1.1452],\n",
      "        [-0.4535, -0.4535, -0.5980, -0.5258, -0.4535, -0.3812,  0.7030,  0.5946,\n",
      "          0.0886, -0.8149, -0.1282, -0.4173, -0.8149, -0.6342, -0.0559, -2.1159,\n",
      "         -1.3208, -1.1763,  0.0525,  0.3778],\n",
      "        [ 0.3968, -0.1671, -0.5618, -0.5054, -0.5336, -0.3363,  0.0867, -0.2517,\n",
      "         -0.0543,  0.1149, -0.4208,  0.3968,  0.1995,  0.3968,  0.7634,  1.0171,\n",
      "          0.7070,  0.9044,  0.4250,  0.3968]])\n",
      "目标数据: tensor([-2.4235,  0.9008])\n",
      "样本 10:\n",
      "输入数据: tensor([[-0.7730, -0.7946, -0.7741, -1.1842, -1.2479, -1.4206, -1.2695, -4.5086,\n",
      "         -0.8388, -0.9036, -0.9252, -0.7093, -0.9241, -0.9457, -0.9457, -0.2549,\n",
      "         -0.0822, -0.8162, -0.9673, -0.9457],\n",
      "        [ 2.1156,  2.1156,  2.1151,  1.7901,  0.5268,  0.8879,  0.5991,  2.4761,\n",
      "          2.0067,  1.9706,  1.6818,  2.0789,  1.9351,  2.0073,  2.0073,  1.1768,\n",
      "          0.4546,  2.0434,  2.1156,  2.0073],\n",
      "        [-0.6090, -0.6350, -0.6353, -0.5572, -0.9999, -0.8175, -0.9478,  0.7461,\n",
      "         -0.5311, -0.5572, -1.6779, -0.2704, -0.5568, -0.5047, -0.4786, -1.5473,\n",
      "         -1.6255, -0.6611, -0.4004, -0.4526]])\n",
      "目标数据: tensor([0.2248, 0.1887])\n"
     ]
    }
   ],
   "source": [
    "# 输出 train_loader 前 10 行数据并计算行数\n",
    "train_rows = 0\n",
    "print(\"train_loader 前 10 行数据：\")\n",
    "for i, batch in enumerate(train_loader):\n",
    "    if i < 10:\n",
    "        print(f\"第 {i + 1} 行: {batch}\")\n",
    "    train_rows += len(batch)\n",
    "\n",
    "# 输出 test_loader 前 10 行数据并计算行数\n",
    "test_rows = 0\n",
    "print(\"\\ntest_loader 前 10 行数据：\")\n",
    "for i, batch in enumerate(test_loader):\n",
    "    if i < 10:\n",
    "        print(f\"第 {i + 1} 行: {batch}\")\n",
    "    test_rows += len(batch)\n",
    "\n",
    "print(f\"\\ntrain_loader 数据行数: {train_rows}\")\n",
    "print(f\"test_loader 数据行数: {test_rows}\")\n",
    "print(\"训练集前十个样本：\")\n",
    "for i in range(min(10, len(train_dataset))):\n",
    "    input_tensor, target_tensor = train_dataset[i]\n",
    "    print(f\"样本 {i + 1}:\")\n",
    "    print(\"输入数据:\", input_tensor)\n",
    "    print(\"目标数据:\", target_tensor)\n",
    "\n",
    "print(\"\\n测试集前十个样本：\")\n",
    "for i in range(min(10, len(test_dataset))):\n",
    "    input_tensor, target_tensor = test_dataset[i]\n",
    "    print(f\"样本 {i + 1}:\")\n",
    "    print(\"输入数据:\", input_tensor)\n",
    "    print(\"目标数据:\", target_tensor)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e773be37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 模型定义（残差网络）\n",
    "class ResidualBlock(nn.Module):\n",
    "    def __init__(self, channels, kernel_size=3, padding=1):\n",
    "        super(ResidualBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv1d(channels, channels, kernel_size, padding=padding)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = nn.Conv1d(channels, channels, kernel_size, padding=padding)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.conv1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.conv2(out)\n",
    "        out += x  # 跳跃连接\n",
    "        out = self.relu(out)\n",
    "        return out\n",
    "class ResNetTimeSeries(nn.Module):\n",
    "    def __init__(self, in_channels, num_filters, num_residual_blocks, seq_len, forecast_horizon, output_dim):\n",
    "        super(ResNetTimeSeries, self).__init__()\n",
    "        # 预处理卷积层：使输入映射到 num_filters 通道\n",
    "        self.pre_conv = nn.Conv1d(in_channels, num_filters, kernel_size=3, padding=1)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        # 残差模块堆叠\n",
    "        self.res_blocks = nn.Sequential(\n",
    "            *[ResidualBlock(num_filters, kernel_size=3, padding=1) for _ in range(num_residual_blocks)]\n",
    "        )\n",
    "        # 将时序特征进行全局池化，将整个序列的信息汇聚\n",
    "        self.global_pool = nn.AdaptiveAvgPool1d(1)\n",
    "        # 预测层：全连接层，将汇聚后的特征映射到输出维度\n",
    "        self.fc = nn.Linear(num_filters, output_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        x: 输入形状 (batch_size, in_channels, seq_len)\n",
    "        \"\"\"\n",
    "        out = self.pre_conv(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.res_blocks(out)\n",
    "        # 全局池化：输出形状 (batch_size, num_filters, 1)\n",
    "        out = self.global_pool(out)\n",
    "        out = out.squeeze(-1)  # 变成 (batch_size, num_filters)\n",
    "        out = self.fc(out)  # (batch_size, output_dim)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af5b0bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义模型参数\n",
    "IN_CHANNELS = dataset.input_data.shape[1]  # 输入特征数\n",
    "NUM_FILTERS = 64  # 隐藏层通道数\n",
    "NUM_RES_BLOCKS = 3  # 残差块数\n",
    "FORECAST_HORIZON = 1  # 单步预测\n",
    "OUTPUT_DIM = dataset.target_data.shape[1]  # 输出维度与预测目标特征数相同\n",
    "# 建模时注意：输入给 1D Conv 的数据形状为 (batch_size, channels, seq_len)\n",
    "\n",
    "model = ResNetTimeSeries(in_channels=IN_CHANNELS, num_filters=NUM_FILTERS,\n",
    "                         num_residual_blocks=NUM_RES_BLOCKS, seq_len=SEQ_LEN,\n",
    "                         forecast_horizon=FORECAST_HORIZON, output_dim=OUTPUT_DIM)\n",
    "model = model.to(device)\n",
    "\n",
    "# 3. 模型训练与优化\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "num_epochs = 150\n",
    "train_losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c7ac186",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练模型...\n",
      "Epoch [10/150], Loss: 0.0967\n",
      "Epoch [20/150], Loss: 0.0719\n",
      "Epoch [30/150], Loss: 0.0588\n",
      "Epoch [40/150], Loss: 0.0536\n",
      "Epoch [50/150], Loss: 0.0471\n",
      "Epoch [60/150], Loss: 0.0443\n",
      "Epoch [70/150], Loss: 0.0411\n",
      "Epoch [80/150], Loss: 0.0395\n",
      "Epoch [90/150], Loss: 0.0378\n",
      "Epoch [100/150], Loss: 0.0358\n",
      "Epoch [110/150], Loss: 0.0357\n",
      "Epoch [120/150], Loss: 0.0331\n",
      "Epoch [130/150], Loss: 0.0330\n",
      "Epoch [140/150], Loss: 0.0326\n",
      "Epoch [150/150], Loss: 0.0317\n",
      "Test MSE: 0.0402\n",
      "Test RMSE: 0.2006\n",
      "Test MAE: 0.1051\n",
      "Test R^2: 0.9596\n"
     ]
    }
   ],
   "source": [
    "print(\"开始训练模型...\")\n",
    "for epoch in range(num_epochs):\n",
    "    epoch_loss = 0.0\n",
    "    model.train()\n",
    "    for batch_x, batch_y in train_loader:\n",
    "        batch_x = batch_x.to(device)\n",
    "        batch_y = batch_y.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(batch_x)\n",
    "        loss = criterion(outputs, batch_y)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item() * batch_x.size(0)\n",
    "    epoch_loss /= len(train_loader.dataset)\n",
    "    train_losses.append(epoch_loss)\n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f\"Epoch [{epoch + 1}/{num_epochs}], Loss: {epoch_loss:.4f}\")\n",
    "\n",
    "# 保存模型\n",
    "torch.save(model, 'result_model.pth')\n",
    "\n",
    "# 保存标准化器\n",
    "scaler = {\n",
    "    'input_mean': dataset.input_mean,\n",
    "    'input_std': dataset.input_std,\n",
    "    'target_mean': dataset.target_mean,\n",
    "    'target_std': dataset.target_std\n",
    "}\n",
    "joblib.dump(scaler, 'scaler.pkl')\n",
    "\n",
    "# 4. 模型评估与结果预测\n",
    "model.eval()\n",
    "predictions = []\n",
    "ground_truths = []\n",
    "with torch.no_grad():\n",
    "    for batch_x, batch_y in test_loader:\n",
    "        batch_x = batch_x.to(device)\n",
    "        batch_y = batch_y.to(device)\n",
    "        preds = model(batch_x)\n",
    "        predictions.append(preds.cpu().numpy())\n",
    "        ground_truths.append(batch_y.cpu().numpy())\n",
    "predictions = np.concatenate(predictions, axis=0)\n",
    "ground_truths = np.concatenate(ground_truths, axis=0)\n",
    "\n",
    "# 计算测试集的评估指标\n",
    "test_mse = mean_squared_error(ground_truths, predictions)\n",
    "test_rmse = np.sqrt(test_mse)\n",
    "test_mae = mean_absolute_error(ground_truths, predictions)\n",
    "test_r2 = r2_score(ground_truths, predictions)\n",
    "\n",
    "print(f\"Test MSE: {test_mse:.4f}\")\n",
    "print(f\"Test RMSE: {test_rmse:.4f}\")\n",
    "print(f\"Test MAE: {test_mae:.4f}\")\n",
    "print(f\"Test R^2: {test_r2:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d0055cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. 结果可视化分析\n",
    "# 为了便于展示，我们随机选取测试集中的 50 个样本\n",
    "num_show = 50\n",
    "idx = np.random.choice(len(predictions), num_show, replace=False)\n",
    "selected_preds = predictions[idx]\n",
    "selected_truths = ground_truths[idx]\n",
    "\n",
    "# 计算残差（预测误差）\n",
    "residuals = selected_truths - selected_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a4810e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制 4 个图（2x2 布局）在同一张图中\n",
    "# 设置图表背景颜色为白色\n",
    "plt.rcParams['axes.facecolor'] = 'white'\n",
    "fig, axs = plt.subplots(2,2, figsize=(16,12))\n",
    "fig.suptitle(\"Analysis Figures for Residual Network Time Series Forecasting\", fontsize=18)\n",
    "\n",
    "# 1. Training Loss Curve\n",
    "axs[0,0].plot(range(1, num_epochs+1), train_losses, color='blue', linewidth=2)\n",
    "axs[0,0].set_title(\"Training Loss Curve\", fontsize=14)\n",
    "axs[0,0].set_xlabel(\"Epoch\", fontsize=12)\n",
    "axs[0,0].set_ylabel(\"MSE Loss\", fontsize=12)\n",
    "axs[0,0].grid(True)\n",
    "\n",
    "# 2. True vs Predicted\n",
    "# 为了更直观对比，我们绘制随机选取的样本预测值与真实值的曲线图\n",
    "x_axis = np.arange(num_show)\n",
    "axs[0, 1].plot(x_axis, selected_truths[:, 0], 'r-', label=\"True Value\", linewidth=2) \n",
    "axs[0, 1].plot(x_axis, selected_preds[:, 0], 'g--', label=\"Predicted Value\", linewidth=2)\n",
    "axs[0, 1].set_title(\"True vs Predicted\", fontsize=14)\n",
    "axs[0, 1].set_xlabel(\"Sample Index\", fontsize=12)\n",
    "axs[0, 1].set_ylabel(\"Value\", fontsize=12)\n",
    "axs[0, 1].legend()\n",
    "axs[0, 1].grid(True)\n",
    "\n",
    "# 3. Residuals Histogram\n",
    "axs[1, 0].hist(residuals[:, 0], bins=15, color='orange', edgecolor='black', alpha=0.8)\n",
    "axs[1, 0].set_title(\"Residuals Histogram\", fontsize=14)\n",
    "axs[1, 0].set_xlabel(\"Residual\", fontsize=12)\n",
    "axs[1, 0].set_ylabel(\"Frequency\", fontsize=12)\n",
    "axs[1, 0].grid(True)\n",
    "\n",
    "# 4. Scatter Plot of Predictions vs. True Values\n",
    "axs[1, 1].scatter(ground_truths[:, 0], predictions[:, 0], color='purple', alpha=0.6, s=50)\n",
    "axs[1, 1].plot([ground_truths[:, 0].min(), ground_truths[:, 0].max()],\n",
    "               [ground_truths[:, 0].min(), ground_truths[:, 0].max()], 'k--', linewidth=2)\n",
    "axs[1, 1].set_title(\"Predicted vs True Scatter\", fontsize=14)\n",
    "axs[1, 1].set_xlabel(\"True Values\", fontsize=12)\n",
    "axs[1, 1].set_ylabel(\"Predicted Values\", fontsize=12)\n",
    "axs[1, 1].grid(True)\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dd3e5ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 定义模型保存路径（可自定义路径和文件名）\n",
    "# model_save_path = \"result_model.pth\"  # 保存在当前目录，文件名为 resnet_time_series_model.pth\n",
    "\n",
    "# # 保存模型参数（推荐方式，文件更小且兼容性更好）\n",
    "# torch.save(model.state_dict(), model_save_path)\n",
    "# print(f\"模型已成功保存至：{model_save_path}\")\n",
    "\n",
    "# # （可选）若需保存整个模型（包括结构），可使用以下代码（不推荐，可能因环境差异导致加载问题）\n",
    "# # torch.save(model, model_save_path)\n",
    "# # print(f\"完整模型已保存至：{model_save_path}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
